Input-Driven Active Testing of Multi-threaded Programs

Han, Yuexin; Wu, Peng; Chen, Tsong Yueh; Lv, Yi

doi:10.1109/apsec.2015.34

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…Our experimental results show that, BC-ConTest can obtain a significant improvement on efficiency without impairing bug finding capacities. In the future, we will combine active testing [32]- [35] to further improve the efficiency of concurrency bug detection. In addition, we plan to extend the proposed approach to the massage-passing parallel programs [36], [37] to improve its generalization.…”

Section: Discussionmentioning

confidence: 99%

Efficient Test Case Generation for Thread-Safe Classes

Jiang

Qian

et al. 2019

IEEE Access

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Generating test cases automatically for thread-safe classes is an effective approach to validating their correctness. However, the existing concurrent test generation techniques usually consume a large amount of time and efforts before finding concurrency bugs. To alleviate this problem, we present an automatic and efficient approach which combines the advantages of both the bug-driven and coverage-guided techniques to generate test cases for thread-safe classes. First, method pairs that cannot be executed concurrently are removed by the static analysis. Then, a strategy of the bug-driven grouping of method pairs is designed to divide the remaining method pairs into two groups. One group contains the method pairs with a high priority, and another group contains the method pairs with a low priority. Finally, iterative generation of concurrent test cases, which consists of the coverage-guided generation of concurrent tests and concurrency bug detection, is conducted to find concurrency bugs. Our evaluation is on 20 thread-safe classes. Compared with four state-of-the-art approaches, the results show that our approach can obtain a significant improvement in efficiency without impairing bug finding capacities.INDEX TERMS Software testing, thread-safe class, test case generation, concurrency bug, static analysis.

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Section: Discussionmentioning

confidence: 99%

Efficient Test Case Generation for Thread-Safe Classes

Jiang

Qian

et al. 2019

IEEE Access

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“…This study chooses to verify the effectiveness of GC-MCR in Java programs. In comparison, other concurrent test methods, such as the active test methods based on saturated coverage, Maple [26] and IDAT [27] , as well as multiple concurrent test methods based on CBMC [28][29][30] , are all proved effective in C programs. SeqCheck [23] detects concurrent bugs in Java programs by modeling program branch information and predicting the feasibility of event sequences.…”

Section: Analysis Of Factors Affecting Effectivenessmentioning

confidence: 99%

“…ConRacer [56] builds a calling graph by control-flow analysis, searches for alias objects by context-sensitive alias analysis, and finally reduces false and missed detection by happensbefore analysis. Maple [26] proposed an active test method based on saturated coverage, and Yue et al [27] proposed a measure of test-case diversity for multithreaded programs based on Maple. Alglave et al [29] proposed a kind of integer differential logic coding, verifying deployed concurrent system code with the help of bounded model checking.…”

Section: Bug Detection Of Concurrent Programsmentioning

confidence: 99%

GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method

Li,

Wang,

et al. 2023

International Journal of Software and Informatics

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Constraint solving has been applied to many domains of program analysis and is further used in concurrent program analysis. Concurrent programs have been widely used with the rapid development of multi-core processors. However, concurrent bugs threaten the security and reliability of concurrent programs, and thus it is of great importance to detect concurrent bugs. The explosion of thread interleaving caused by the uncertainty of the execution of concurrent program threads brings some challenges to the detection of concurrent bugs. Existing concurrent defect detection algorithms reduce the exploration cost in the state space of concurrent programs by reducing invalid thread interleaving. For example, the maximal causal model algorithm transforms the state space exploration problem of concurrent programs into a constraint solving problem. However, it will produce a large number of redundant and conflicting constraints during constraint construction, which greatly prolongs the time of constraint solving, increases the number of constraint solver calls, and reduces the exploration efficiency of concurrent program state space. Thus, this study proposes a directed graph constraint-guided maximal causality reduction method, called GC-MCR. This method aims to improve the speed of constraint solving and the efficiency of the state space exploration of concurrent programs by filtering and reducing constraints using directed graphs. The experimental results show that the GC-MCR method can effectively optimize the expression of constraints, so as to improve the solving speed of the constraint solver and reduce the number of solver calls. Compared with the existing J-MCR method, GC-MCR can significantly improve the detection efficiency of concurrent program bugs without reducing the detection ability of concurrent bugs, and the test time on 38 groups of concurrent test programs widely used by existing research methods can be reduced by 34.01% on average.

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“…Based on FSCS-ART [9], Yue et al [191] proposed two input-driven active testing approaches for multi-threaded programs, with experimental evaluations indicating that the proposed methods are more cost-effective than traditional active testing. Similarly, Sim et al [192] applied FSCS-ART [9] to fuzzing the Out-Of-Memory (OOM) Killer on an embedded Linux distribution, with results showing that their ART approach for fuzzing requires significantly fewer test cases than RT to identify an OOM Killer failure.…”

Section: Active Fuzzing and Integration Testingmentioning

confidence: 99%

A Survey on Adaptive Random Testing

Huang

Sun

et al. 2021

IIEEE Trans. Software Eng.

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Random testing (RT) is a well-studied testing method that has been widely applied to the testing of many applications, including embedded software systems, SQL database systems, and Android applications. Adaptive random testing (ART) aims to enhance RT's failure-detection ability by more evenly spreading the test cases over the input domain. Since its introduction in 2001, there have been many contributions to the development of ART, including various approaches, implementations, assessment and evaluation methods, and applications. This paper provides a comprehensive survey on ART, classifying techniques, summarizing application areas, and analyzing experimental evaluations. This paper also addresses some misconceptions about ART, and identifies open research challenges to be further investigated in the future work.

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Input-Driven Active Testing of Multi-threaded Programs

Cited by 5 publications

References 28 publications

Efficient Test Case Generation for Thread-Safe Classes

Efficient Test Case Generation for Thread-Safe Classes

GC-MCR: Directed Graph Constraint-guided Concurrent Bug Detection Method

A Survey on Adaptive Random Testing

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